Ad Tech

Ad tech models map content to the IAB Content Taxonomy and the audience signals that programmatic advertising runs on. ZeroGPU offers a base IAB classifier tuned for real-time categorization at the edge, an enriched variant that adds topics, keywords, and inferred intent, and a signal extractor that turns free text into structured targeting signals. Each request is shown for both the Responses API and the OpenAI-compatible Chat Completions API.

Model	Input /1M	Output /1M	Max tokens
`zlm-v1-iab-classify-edge`	$0.02	$0.05	400
`zlm-v2-iab-classify-edge-enriched`	$0.025	$0.15	800
`zlm-v1-iab-domain-classifier`	$0.02	$0.05	100
`zlm-v1-signal-extract`	$0.02	$0.05	400

zlm-v1-iab-classify-edge

ZeroGPU’s IAB classifier maps any text to the industry-standard IAB Content Taxonomy in a single, fast inference call. Each call returns categories across both the 1.0 and 2.2 taxonomies plus matched audience segments, every result scored for confidence. With support for 50+ languages, it classifies multilingual content without a translation step. At 90M parameters on ONNX, it’s built for the high-volume, sub-millisecond classification that ad tech and content platforms demand, right at the edge. When you need clean category and audience signals on every request, this is the model — and when you need the full profile, reach for the enriched variant.

References: Model docs • Terms • Privacy

curl https://api.zerogpu.ai/v1/responses \
  -H "content-type: application/json" \
  -H "x-api-key: YOUR_API_KEY" \
  -H "x-project-id: YOUR_PROJECT_ID" \
  -d '{
  "input": "Technology has quietly reshaped the rhythm of everyday life, weaving itself into routines so seamlessly that it often goes unnoticed. From the moment a smartphone alarm wakes someone in the morning to the final glance at a glowing screen before sleep, digital systems guide communication, navigation, work, and entertainment. This transformation did not happen overnight. It emerged through decades of incremental innovation, each new tool building upon the last, until the modern world became deeply interconnected.One of the most significant changes has been the speed at which information travels.",
  "model": "zlm-v1-iab-classify-edge"
}'

curl https://api.zerogpu.ai/v1/chat/completions \
  -H "content-type: application/json" \
  -H "x-api-key: YOUR_API_KEY" \
  -H "x-project-id: YOUR_PROJECT_ID" \
  -d '{
  "model": "zlm-v1-iab-classify-edge",
  "messages": [
    {
      "role": "user",
      "content": "What are the basics of exercise and physical fitness?Exercise is anything that gets your body moving. Regular exercise is one of the best things you can do for your health. It has many benefits, including improving your overall health and fitness, and reducing your risk for many chronic (long-term) diseases.Every physical fitness routine is built on a few simple ideas. These include:Make exercise a habit, as your body adapts to the type of activity you do most often. Regular practice will help you improve.Build up your activity level slowly to help you continue to get stronger, faster, or more flexible without pushing too hard all at once.Challenge yourself by lifting slightly heavier weights, adding a few more minutes to your walk, or increasing your pace."
    }
  ]
}'

Returns IAB audience categories plus content matches across taxonomy versions, each with a confidence score.

Response

{
  "audience": [
    { "name": "Technology & Computing", "score": 0.7686 },
    { "name": "Consumer Electronics", "score": 0.7206 },
    { "name": "65-69", "score": 0.5950 }
  ],
  "content": {
    "iab_1_0": [
      { "name": "Cell Phones", "score": 0.7597 },
      { "name": "Technology & Computing", "score": 0.7387 }
    ],
    "iab_2_2": [
      { "name": "Smartphones", "score": 0.7597 },
      { "name": "Wearable Technology", "score": 0.7387 },
      { "name": "Technology & Computing", "score": 0.7109 }
    ]
  }
}

zlm-v2-iab-classify-edge-enriched

The enriched variant of ZeroGPU’s IAB classifier turns a single inference call into a full content-intelligence profile not just a label, but everything a contextual pipeline needs to act on. Each call returns IAB categories across both the 1.0 and 2.2 taxonomies (down to tier-3), audience and interest segments, topics, keywords, and user-intent classification — all with confidence scores with edge-native, sub-millisecond speeds. With support for 50+ languages, it delivers the same full profile on multilingual content without a translation step. Built for contextual ad targeting, brand-safety scoring, publisher content categorization, and signal pipelines that need full metadata on every request.

References: Model docs • Terms • Privacy

curl https://api.zerogpu.ai/v1/responses \
  -H "content-type: application/json" \
  -H "x-api-key: YOUR_API_KEY" \
  -H "x-project-id: YOUR_PROJECT_ID" \
  -d '{
  "input": "Technology has quietly reshaped the rhythm of everyday life, weaving itself into routines so seamlessly that it often goes unnoticed. From the moment a smartphone alarm wakes someone in the morning to the final glance at a glowing screen before sleep, digital systems guide communication, navigation, work, and entertainment. This transformation did not happen overnight. It emerged through decades of incremental innovation, each new tool building upon the last, until the modern world became deeply interconnected.One of the most significant changes has been the speed at which information travels.",
  "model": "zlm-v2-iab-classify-edge-enriched"
}'

curl https://api.zerogpu.ai/v1/chat/completions \
  -H "content-type: application/json" \
  -H "x-api-key: YOUR_API_KEY" \
  -H "x-project-id: YOUR_PROJECT_ID" \
  -d '{
  "model": "zlm-v2-iab-classify-edge-enriched",
  "messages": [
    {
      "role": "user",
      "content": "What are the basics of exercise and physical fitness?Exercise is anything that gets your body moving. Regular exercise is one of the best things you can do for your health. It has many benefits, including improving your overall health and fitness, and reducing your risk for many chronic (long-term) diseases.Every physical fitness routine is built on a few simple ideas. These include:Make exercise a habit, as your body adapts to the type of activity you do most often. Regular practice will help you improve.Build up your activity level slowly to help you continue to get stronger, faster, or more flexible without pushing too hard all at once.Challenge yourself by lifting slightly heavier weights, adding a few more minutes to your walk, or increasing your pace."
    }
  ]
}'

In addition to scored audience and content categories, the enriched model returns taxonomy codes and parent IDs, plus topics, keywords, and an inferred user_intent.

Response

{
  "audience": [
    {
      "id": 687,
      "parent_id": 206,
      "name": "Technology & Computing",
      "tier1_name": "Interest",
      "tier2_name": "Technology & Computing",
      "score": 0.7686
    },
    {
      "id": 703,
      "parent_id": 687,
      "name": "Consumer Electronics",
      "tier1_name": "Interest",
      "tier2_name": "Technology & Computing",
      "tier3_name": "Consumer Electronics",
      "score": 0.7206
    }
  ],
  "content": {
    "iab_1_0": [
      { "code": "IAB19-6", "name": "Cell Phones", "tier": 2, "parent_code": "IAB19", "score": 0.7597 },
      { "code": "IAB19", "name": "Technology & Computing", "tier": 1, "parent_code": null, "score": 0.7387 }
    ]
  },
  "topics": [
    { "name": "smartphones", "score": 0.7597 },
    { "name": "wearable technology", "score": 0.7387 },
    { "name": "technology & computing", "score": 0.7109 }
  ],
  "keywords": ["technology", "reshaped", "everyday", "routines", "quietly", "weaving", "rhythm", "itself", "life", "into"],
  "user_intent": {
    "name": "mastering technology quietly reshaped rhythm everyday life",
    "category": "informational",
    "score": 0.5
  }
}

zlm-v1-iab-domain-classifier

ZeroGPU’s Domain IAB Classifier maps a raw domain name straight to the IAB Content Taxonomy in a single, fast inference call, returning content categories, topics, keywords, and user-intent signals. Because it needs only the domain as input, it reduces payload size by up to 10x compared to page-level classification workflows while still surfacing the contextual signals that bidstream enrichment, contextual targeting, and domain-level intelligence pipelines run on. At 149M parameters on ONNX, it’s built for the high-volume, sub-millisecond classification that ad tech and content platforms demand, right at the edge.

References: Model docs • Terms • Privacy

Domain-level classification

Page-level classifiers like zlm-v1-iab-classify-edge and zlm-v2-iab-classify-edge-enriched take the full body of an article or page as input and map that content to the taxonomy. zlm-v1-iab-domain-classifier works one level up: it takes only the raw domain name (for example nytimes.com) and infers the categories, topics, keywords, and intent that characterize the site as a whole. That shift has two practical effects:

Up to 10x smaller payloads. You send a single hostname instead of crawling and shipping page text, which makes it well suited to bidstream enrichment, where the domain is often the only contextual signal available and per-request size matters.
No page fetch required. Use it when you have the domain but not the rendered content — domain-level intelligence pipelines, allow/deny list scoring, and contextual targeting at the inventory level. When you do have the page text and need per-URL precision, reach for the page-level classifiers instead.

curl https://api.zerogpu.ai/v1/responses \
  -H "content-type: application/json" \
  -H "x-api-key: YOUR_API_KEY" \
  -H "x-project-id: YOUR_PROJECT_ID" \
  -d '{
  "input": "nytimes.com",
  "model": "zlm-v1-iab-domain-classifier"
}'

curl https://api.zerogpu.ai/v1/chat/completions \
  -H "content-type: application/json" \
  -H "x-api-key: YOUR_API_KEY" \
  -H "x-project-id: YOUR_PROJECT_ID" \
  -d '{
  "model": "zlm-v1-iab-domain-classifier",
  "messages": [
    {
      "role": "user",
      "content": "nytimes.com"
    }
  ]
}'

Returns scored content categories across taxonomy versions plus topics, keywords, and an inferred user_intent for the domain.

Response

{
  "content": {
    "iab_1_0": [
      { "name": "News", "score": 0.8124 },
      { "name": "Politics", "score": 0.6533 }
    ],
    "iab_2_2": [
      { "name": "News and Politics", "score": 0.8124 },
      { "name": "Business and Finance", "score": 0.5980 }
    ]
  },
  "topics": [
    { "name": "news", "score": 0.8124 },
    { "name": "journalism", "score": 0.6740 },
    { "name": "current events", "score": 0.6533 }
  ],
  "keywords": ["news", "politics", "world", "business", "opinion", "media", "journalism"],
  "user_intent": {
    "name": "reading news and current events",
    "category": "informational",
    "score": 0.5
  }
}

zlm-v1-signal-extract

ZeroGPU’s signal extractor turns unstructured text into structured signals that downstream systems can act on. One inference call returns topics, keywords, intent, and other contextual attributes as structured output, so content enrichment, contextual intelligence, ad targeting, agent routing, recommendation systems, and analytics pipelines all read from the same pass. At 80M parameters it is built for high-volume workloads where a general-purpose LLM is more machinery than the job needs.

References: Terms • Privacy Pass the signal categories you want in categories; max_keywords caps how many keywords come back.

curl https://api.zerogpu.ai/v1/responses \
  -H "content-type: application/json" \
  -H "x-api-key: YOUR_API_KEY" \
  -H "x-project-id: YOUR_PROJECT_ID" \
  -d '{
  "model": "zlm-v1-signal-extract",
  "input": "How can I pay using my credit card?",
  "categories": [
    "signals"
  ]
}'

curl https://api.zerogpu.ai/v1/chat/completions \
  -H "content-type: application/json" \
  -H "x-api-key: YOUR_API_KEY" \
  -H "x-project-id: YOUR_PROJECT_ID" \
  -d '{
  "model": "zlm-v1-signal-extract",
  "messages": [
    {
      "role": "user",
      "content": "How can I pay using my credit card?"
    }
  ],
  "categories": [
    "signals"
  ],
  "max_keywords": 6
}'

The signals arrive as a JSON string in the output text. Parsed:

Response

{
  "keywords": [
    "credit card",
    "login",
    "official website",
    "account access"
  ],
  "user_intent": {
    "name": "pay using credit card",
    "category": "navigational",
    "score": 0.95
  }
}

Get Started

Models

Guides

Platform

zlm-v1-iab-classify-edge

zlm-v2-iab-classify-edge-enriched

zlm-v1-iab-domain-classifier

Domain-level classification

zlm-v1-signal-extract

​zlm-v1-iab-classify-edge

​zlm-v2-iab-classify-edge-enriched

​zlm-v1-iab-domain-classifier

​Domain-level classification

​zlm-v1-signal-extract

zlm-v1-iab-classify-edge

zlm-v2-iab-classify-edge-enriched

zlm-v1-iab-domain-classifier

Domain-level classification

zlm-v1-signal-extract